An alignment-free approach to cluster proteins using frequency of conserved k-mers

Abstract

Identifying conserved regions in protein sequences is a fundamental operation that is recurrent in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, compute protein clusters, annotate sequences with function, and compute evolutionary relationships among protein sequences. Current approaches to clustering and annotating protein sequences based on conserved regions depend either on prior knowledge of domains or on computing pairwise sequence similarity, which is not feasible for very large collections of protein sequences. In this paper we present a new clustering method, afClust, that uses the abundance of exact matching short subsequences (k-mers) to quickly detect conserved regions. Our method also lends itself to parallelization under the MapReduce paradigm. Our experimental results are promising. For bacterial protein domains from the SMART database, we detected up to 85% of the targeted domain regions. Our parallel implementation processed 700,000 sequences in approximately one minute. We provide scalability experiments for a smaller data set.